Fruit-tree canopies are typically characterized by complex branching architecture and dense foliage, leading to severe self-occlusion, uneven light distribution, and low light-use efficiency. However, experience-driven decisions cannot fully meet the large-scale pruning of the standard canopy shapes and optimal regulation of tree vigor in the orchard. In this study, an intelligent pruning model was proposed to optimize the canopy structure reconstruction and light-efficiency evaluation using Neural Radiance Fields (NeRF). A reproducible, quantitative, and visualization-friendly workflow was also provided for canopy analysis and pruning under a real orchard. Qingcuili plum (Prunus salicina cv. ‘Qingcuili’) was selected as the target species. Multi-view videos were captured around each tree from multiple angles for sufficient coverage of the canopy under field lighting and background. A NeRF reconstruction was used to learn volumetric radiance and density fields from the video frames. A high-fidelity 3D representation of the tree was generated from the reconstructed structure. Branch topology and geometric descriptors (e.g., branch order, orientation, length, and spatial distribution) were extracted for decision-making. The Monte Carlo Ray Tracing (MCRT) module was integrated to simulate ray–canopy interactions. Both direct and diffuse radiation pathways were estimated to quantify light conditions in the 3D canopy space. Two indicators were computed: light interception ratio (LIR) to characterize the proportion of incident light intercepted by the canopy, and energy interception ratio (EIR) to reflect the effective energy under the simulated radiation field. A pruning recommendation model was constructed to fuse: (i) branch-classification and geometric features, and (ii) light-efficiency weights derived from the MCRT outputs. Candidate-branch suggestions were given for the improved canopy illumination with structural feasibility. In addition, a virtual interactive pruning system was developed to support human-in-the-loop validation, intuitive visualization of light distribution, and pruning effects before field implementation. The framework was validated on a dataset of 102 Qingcuili plum trees with diverse canopies and growth. The NeRF-based reconstruction was achieved with high accuracy, with an average reconstruction error of 4.3%, indicating reliable recovery of canopy structure under complex occlusion. The pruning recommendation performance reached 93.2% accuracy, compared with expert labelling. Pruning recommendations were consistent with practical knowledge. The optimal pruning strategy was applied to substantially improve the canopy light conditions. LIR and EIR increased by approximately 15.2% and 18.9%, respectively, indicating enhanced light interception and more effective energy capture. From a deployment perspective, the end-to-end system response time remained stable in 2.3 min using cloud GPU acceleration (NVIDIA V100), indicating favorable real-time applicability for decision support and interactive analysis. A canopy structure–light-efficiency optimization was integrated with NeRF 3D reconstruction, MCRT light simulation, and feature–weight fusion for intelligent pruning. The 3D canopy reconstruction and quantitative pruning improved the light-use efficiency and the precision of tree vigor regulation in a real orchard. The finding can provide a feasible technical pathway toward digital twins and smart pruning for fruit-tree production.